Three major approaches have been taken to define non-classical multidrug resistance in cancer. In the first, we isolate KB cells and ovarian cancer cells resistant to increasing levels of cisplatin (CP-r) and demonstrate multidrug resistance to many other cytotoxic agents. In some cases, this cross-resistance pattern is due to reduced uptake of each of these agents because their receptors have been relocalized from the cell surface into the cytoplasm of the cell. This relocalization of surface transporters appears to be due to altered recycling of these transporters due to alterations in the cytoskeleton that affect endocytic recycling compartments in cisplatin-resistant cells. Recent studies on cisplatin-resistant cell lines derived from cisplatin-resistant human cancers indicate that reduced cisplatin accumulation is not an obligatory characteristic of resistant tumors. We are undertaking a complete genomic analysis using RNA-seq, ATAC-seq and Pro-seq technologies to define the alterations in gene expression that accompany the development of drug resistance in cisplatin-selected cell lines. These will be compared to gene expression changes in clinical samples of serous ovarian cancer and small cell lung cancers for which the primary treatment involves cisplatin as a cytotoxic agent. Our interest in the checkpoint kinases and their role in resistance led us to the protein phosphatase 2A (PP2A) inhibitor LB100, currently in Phase I clinical trials for breast cancer. As PP2A controls the phosphorylation status of a number of DNA-damage response (DDR) genes, we hypothesized that LB100 would sensitize ovarian cancer cells to cisplatin. We demonstrated that inhibition of PP2A by LB100 sensitized cells (OVCAR8 and SKOV3) to cisplatin, and that LB100 induces hyperphosphorylation of Chk1 and other genes in the DNA-damage response pathway, preventing cisplatin-induced G2 arrest and forcing cells into mitosis, resulting in apoptosis. We showed that mice injected intraperitoneally with SKOV3-luciferase cells were sensitized to cisplatin (3 mg/kg) when treated with LB100 compared with control. We recently also completed an RNAi screen in cells exposed to cisplatin, in order to identify genes associated with cisplatin sensitivity. If cells exposed to sub-toxic cisplatin undergo cell death when a particular gene is deleted, one can hypothesize that inhibition of this gene target might prove to be a useful adjuvant for platinum chemotherapy. The strongest sensitizing effects were observed when DNA damage repair genes (including a phosphoprotein phosphatase) were silenced, and several of these are now being investigated for their role in cisplatin tolerance. In this screening context, we found a need to identify a solvent appropriate for dissolving cisplatin for screening. We recently showed that DMSO inactivated the biological activity of all clinical and experimental platinum complexes tested. Furthermore, a review of the cisplatin literature revealed that about a third of all research papers have used cisplatin dissolved in DMSO, calling into question the data and conclusions of those papers. This has important implications for the reliability of a significant portion of the literature and points the way for appropriate use of platinum drugs in research. In another approach, we have developed a Taqman Low Density Array (TLDA) microfluidic chip to detect mRNA expression of 380 different putative drug resistance genes and demonstrated that it is a sensitive, accurate, reproducible, and robust way to measure mRNA levels in tumor samples. Previous work from our laboratory indicates that mRNA measurements of levels of drug-resistance genes are, to a first approximation, predictive of functional expression of drug-resistance mechanisms. This drug-resistance chip has been applied to analysis of human cancers. One result from this analysis is that existing cancer cell lines do not mimic the expression patterns of actual human cancers for the 380 putative drug resistance genes chosen for the TLDA analysis and the simple expedient of growing cells in 3D culture does not correct this problem. This suggests the need for better in vitro cancer cell models to study multidrug resistance. Another conclusion is that a signature of eleven MDR genes we have studied predicts poor response in non-effusion ovarian cancer, and different subsets of 18 MDR genes predict poor response in ovarian cancer with effusions. For hepatoma, two different MDR gene expression signatures are associated with poor prognosis and better prognosis hepatoma. Specific drugs that are histone deacetylase inhibitors can convert the pattern of gene expression of poor prognosis hepatomas into cancers with improved prognosis. More detailed studies in collaboration with Drs. Susan Bates, Robert Robey and colleagues indicate that synergistic killing can be achieved with HDAC inhibition in cells harboring mutant Ras by targeting pathways downstream of Ras signaling pathways. Validation of these results, indicating that MDR is complex and multifactorial in clinical cancers, will require the development of reliable in vitro culture models, and interpretation of these data using mathematical models based on network theory is proceeding. Towards this goal, we have developed a bioreactor that mimics capillary delivery (through silicon hydrogels) of oxygen to cells grown in 3D suspension. We have demonstrated physiological oxygen gradients and altered growth of cancer cells more closely approximating in vivo phenotypes. Evidence that oxygen gradients substantially change gene expression patterns has been obtained. The bioreactor can be scaled up for growth of multiple cultures of primary cancer cells or cultured cancer cells to determine whether growth conditions play a primary role in affecting patterns of drug resistance. Because of the apparent complexity of drug resistance mechanisms in vivo, we have re-examined existing mathematical models that predict the development of drug resistance based on more homogeneous systems. In collaboration with Doron Levy (University of Maryland), we have formulated a new mathematical model that takes into account tumor heterogeneity and other features associated with in vivo systems. This model allows a prediction of how chemotherapy should be targeted to optimize killing of drug-resistant cancer cells. In addition, we have shown mathematically that the shape of drug killing curves in cultured cells results from local cell interactions and variations in cell density. These mathematical models promise to inform new approaches to the treatment of multidrug-resistant cancer.